Clinical implications of family history of prostate cancer and genetic risk single nucleotide polymorphism (SNP) profiles in an active surveillance cohort.

OBJECTIVES: To explore the potential prognostic role of family history (FH) of prostate cancer and prostate cancer risk single nucleotide polymorphisms (SNPs) in patients undergoing active surveillance (AS) for prostate cancer. This is the first study to date, which has investigated the potential prognostic role of SNP profiles in an AS cohort PATIENTS AND METHODS: FH data were collected from patients in the Royal Marsden Hospital AS study. In all, 39 prostate cancer-risk SNPs identified from published genome wide association studies (GWAS) were genotyped using the Sequenom Platform and TaqMan™ assays from available DNA. The cumulative genetic-risk scores for each patient were then calculated using the weighted effect estimated from previous GWAS (log-additive model). FH status and the genetic-risk scores were assessed against adverse outcomes in AS, time to treatment and adverse histology on repeat biopsy, using univariable and multivariable Cox regression models to address time to treatment; and binary logistic regression to address biopsy upgrade. RESULTS: Of 471 patients, 55 (13.6%) had adverse histology on repeat biopsies and 145 (30.8%) had deferred treatment. On univariate analysis, there was no significant relationship between FH of prostate cancer in any degree of relation, and adverse histology or time to treatment. For risk score analyses, 386 patients' DNA was studied; and there was also no relationship found between the calculated genetic risk scores and adverse histology or time to treatment (P = 0.573 and P = 0.965, respectively). The retrospective study design and the few events were the main limitation of the study. CONCLUSIONS: There is currently insufficient data to support the use of FH status or prostate cancer SNP profile risk scores as prognostic factors in AS and these should not be used to influence management decisions. As more genetic variants are discovered this may change and should be reassessed in multicentre AS cohorts.

A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact.

Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, , and the residual polygenic component due to the postulated but as yet unknown genetic variants, . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist.

Prostate cancer segregation analyses using 4390 families from UK and Australian population-based studies.

Familial aggregation of prostate cancer is likely to be due to multiple susceptibility loci, perhaps acting in conjunction with shared lifestyle risk factors. Models that assume a single mode of inheritance may be unrealistic. We analyzed genetic models of susceptibility to prostate cancer using segregation analysis of occurrence in families ascertained through population-based series totaling 4390 incident cases. We investigated major gene models (dominant, recessive, general, X-linked), polygenic models, and mixed models of susceptibility using the pedigree analysis software MENDEL. The hypergeometric model was used to approximate polygenic inheritance. The best-fitting model for the familial aggregation of prostate cancer was the mixed recessive model. The frequency of the susceptibility allele in the population was estimated to be 0.15 (95% confidence interval (CI) 0.11-0.20), with a relative risk for homozygote carriers of 94 (95% CI 46-192), and a polygenic standard deviation of 2.01 (95% CI 1.72-2.34). These analyses suggest that one or more genes having a strong recessively inherited effect on risk, as well as a number of genes with variants having small multiplicative effects on risk, may account for the genetic susceptibility to prostate cancer. The recessive component would predict the observed higher familial risk for siblings of cases than for fathers, but this could also be due to other factors such as shared lifestyle by siblings, targeted screening effects, and/or non-additive effects of one or more genes.

Identification of new genetic risk factors for prostate cancer.

There is evidence that a substantial part of genetic predisposition to prostate cancer (PCa) may be due to lower penetrance genes which are found by genome-wide association studies. We have recently conducted such a study and seven new regions of the genome linked to PCa risk have been identified. Three of these loci contain candidate susceptibility genes: MSMB, LMTK2 and KLK2/3. The MSMB and KLK2/3 genes may be useful for PCa screening, and the LMTK2 gene might provide a potential therapeutic target. Together with results from other groups, there are now 23 germline genetic variants which have been reported. These results have the potential to be developed into a genetic test. However, we consider that marketing of tests to the public is premature, as PCa risk can not be evaluated fully at this stage and the appropriate screening protocols need to be developed. Follow-up validation studies, as well as studies to explore the psychological implications of genetic profile testing, will be vital prior to roll out into healthcare.

Multiple loci on 8q24 associated with prostate cancer susceptibility.

Previous studies have identified multiple loci on 8q24 associated with prostate cancer risk. We performed a comprehensive analysis of SNP associations across 8q24 by genotyping tag SNPs in 5,504 prostate cancer cases and 5,834 controls. We confirmed associations at three previously reported loci and identified additional loci in two other linkage disequilibrium blocks (rs1006908: per-allele OR = 0.87, P = 7.9 x 10(-8); rs620861: OR = 0.90, P = 4.8 x 10(-8)). Eight SNPs in five linkage disequilibrium blocks were independently associated with prostate cancer susceptibility.

Identification of seven new prostate cancer susceptibility loci through a genome-wide association study.

Prostate cancer (PrCa) is the most frequently diagnosed cancer in males in developed countries. To identify common PrCa susceptibility alleles, we previously conducted a genome-wide association study in which 541,129 SNPs were genotyped in 1,854 PrCa cases with clinically detected disease and in 1,894 controls. We have now extended the study to evaluate promising associations in a second stage in which we genotyped 43,671 SNPs in 3,650 PrCa cases and 3,940 controls and in a third stage involving an additional 16,229 cases and 14,821 controls from 21 studies. In addition to replicating previous associations, we identified seven new prostate cancer susceptibility loci on chromosomes 2, 4, 8, 11 and 22 (with P = 1.6 x 10(-8) to P = 2.7 x 10(-33)).

Multiple newly identified loci associated with prostate cancer susceptibility.

Prostate cancer is the most common cancer affecting males in developed countries. It shows consistent evidence of familial aggregation, but the causes of this aggregation are mostly unknown. To identify common alleles associated with prostate cancer risk, we conducted a genome-wide association study (GWAS) using blood DNA samples from 1,854 individuals with clinically detected prostate cancer diagnosed at

Multiple loci with different cancer specificities within the 8q24 gene desert.

Recent studies based on genome-wide association, linkage, and admixture scan analysis have reported associations of various genetic variants in 8q24 with susceptibility to breast, prostate, and colorectal cancer. This locus lies within a 1.18-Mb region that contains no known genes but is bounded at its centromeric end by FAM84B and at its telomeric end by c-MYC, two candidate cancer susceptibility genes. To investigate the associations of specific loci within 8q24 with specific cancers, we genotyped the nine previously reported cancer-associated single-nucleotide polymorphisms across the region in four case-control sets of prostate (1854 case subjects and 1894 control subjects), breast (2270 case subjects and 2280 control subjects), colorectal (2299 case subjects and 2284 control subjects), and ovarian (1975 case subjects and 3411 control subjects) cancer. Five different haplotype blocks within this gene desert were specifically associated with risks of different cancers. One block was solely associated with risk of breast cancer, three others were associated solely with the risk of prostate cancer, and a fifth was associated with the risk of prostate, colorectal, and ovarian cancer, but not breast cancer. We conclude that there are at least five separate functional variants in this region.